TigerGraph, a company that provides a graph database and analytics software, has expanded its data science library with 20 new algorithms, bringing its total to more than 50 algorithms.
Graph databases like TigerGraph have become increasingly popular. They are particularly effective at letting data scientists analyze relationships among millions or billions of entities, and they outperform other types of databases for many deep learning applications.
Of course this promising market sees plenty of competitors — startups like Neo4j, MongoDB, and DataStax, as well as giants like Oracle and Amazon. What sets TigerGraph’s product apart is that it is open source, in-database, scalable, and uniquely centered around graph data science.
This company is first to offer a distributed native graph database as well, and has gained traction in enterprises. Graph technologies are predicted to be used in 80% of data and analytics innovations by 2025, according to research firm Gartner, up from just 10% this year.
TigerGraph says that graph will become accessible to more organizations. Not only data scientists, but also business users, can dive deeper into their real-time data with benefits like enterprise-grade scalability, management, and security.
The new group of algorithms can span across various industries, like advertising, financial services, and health care sciences. Within these verticals, TigerGraph has adapted numerous data analytics, data science, and machine learning use cases, including fraud detection, cybersecurity threat detection, energy management, risk assessment and monitoring, and time series analysis.
However, this new release brings more and newer algorithms that are further improved by new groupings by algorithm category. Some will be able to run graph embedding processes either faster or more accurately, for example, which could free up time for data scientists and machine learning engineers. Ultimately, this freed-up time could be spent engaging with other algorithms, like the enhanced groups of community algorithms that can be used for offering personalized recommendations or detecting social groups.
Next up for the Graph Data Science Library will be in-database neural networks to complement its current long list of algorithms. TigerGraph says this future update will simplify your pipeline, while saving time and costs.
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